Neutrino Flight Times in Cosmology

If neutrinos have a small but non-zero mass, time-of-flight effects for neutrino bursts from distant sources can yield information on the large-scale geometry of the universe, the effects being proportional to the integral over time of the cosmological expansion parameter. In principle absolute physical determinations of the Hubble constant and the acceleration parameter are possible. Practical realization of these possibilities would depend on neutrino masses being in a favorable range and the future development of very large detectors.Comment: Three pages, no figure

Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed

Application of Generalized Partial Volume Estimation for Mutual Information based Registration of High Resolution SAR and Optical Imagery

Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance has been thoroughly explored for registration of high resolution SAR (TerraSAR-X) and optical (IKONOS-2) satellite images. Our experiments highlight possibility of an inconsistent MI behavior with different joint histogram bin size which gets reduced with an increase in order of B-spline kernel employed in GPVE. In general, bin size reduction and/or increasing B-spline order have a smoothing affect on MI surfaces and even the lowest order B-spline with a suitable histogram bin size can achieve same pixel level accuracy as achieved by the higher order kernels more consistently

Classification accuracy increase using multisensor data fusion

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc

Vascular plants near the margins of their range in Cedarburg Bog. Part 1. Gymnosperms and Monocots

Marginal populations are those located at the extreme or periphery of a species\u27 range. In the context of this paper, marginal populations refer to a geographical periphery rather than to possible ecological margins. A wide ranging species may be composed of several different varieties or ecotypes. Marginal populations of plants are of special interest to plant taxonomists, ecologists, ecological geneticists and biogeographers because they may exhibit different characteristics than more centrally located populations. This is likely because plants at the boundaries of their species\u27 range may experience extreme ecological conditions beyond which they cannot survive

Accuracy assessment of Digital Surface Models generated by Semiglobal matching algorithm using Lidar data

To measure the accuracy of Digital Surface Models (DSMs) generated by high resolution satellite images (HRSI) using semi-global matching algorithm in comparison with LIDAR DSMs, two different test areas with different properties and corresponding attributes and magnitudes of errors are considered. Error characteristics are classified as systematic and gross errors and significance of them to measure the accuracy of DSMs are evaluated. In this manner and to avoid the influence of outliers in accuracy assessment robust statistical methods are proposed. According to final values obtained for two test areas it can be concluded that the performance of DSMs generated by stereo matching for mountainous wooden areas in respect to the accuracy of LIDAR DSM are poor. In contrast, in case of residential urban areas the quality of the DSM generated by HRSI is able to follow the accuracy of LIDAR data

Vascular plants near the margins of their range in Cedarburg Bog. Part II. Dicots

There are two species of gymnosperms and 18 monocots that are near the southern edge of their geographic range in Cedarburg Bog (Reinartz and Reinartz 1981). Six of these may actually reach their range boundary in the bog. Nine species of the Cyperaceae and seven Orchidaceae comprise the bulk of the monocot species that are near their southern limits. The purpose of this paper is to provide an annotated listing of dicot species which have geographically marginal populations in Cedarburg Bog

Simulation and Analysis of Protein-Fluorophore Systems for Comparison with Fluorescence Spectroscopy Data

Proteine sind die Grundbausteine des Lebens auf molekularer Ebene und wichtig für viele biologische Funktionen wie den Transport von Molekülen, Zellbewegungen oder die Katalyse von chemischen Reaktionen. So transportiert das Protein Hämoglobin beispielsweise den Sauerstoff im menschlichen Blut. Störungen der Proteinfunktionen können schwere degenerative Krankheiten wie zum Beispiel die Parkinson-, Huntington- oder Alzheimer-Krankheit verursachen. Das Verständnis von Proteinfunktionen, ihrer Struktur und Dynamik ist daher ein wichtiges Forschungsgebiet in Biologie, Pharmakologie und Medizin. Da Proteine aufgrund ihrer geringen Größe nicht mit Lichtmikroskopie beobachtet werden können, verwendet man stattdessen indirekte Methoden. Eine dieser Methoden macht sich den Förster-Resonanzenergietransfer (FRET) zunutze, um damit Proteindynamik und andere molekulare Prozesse in vitro und in vivo zu untersuchen. Die Methode wird außerdem auch in Biosensoren zur Messung der Konzentrationen von kleinen Biomolekülen wie zum Beispiel Glukose eingesetzt. Die dabei verwendeten Systeme aus Proteinen und Fluorophoren unterliegen physikalischen Prozessen wie Molekulardynamik und Photophysik. Da man diese Mechanismen nicht direkt beobachten kann, ist die Funktionsweise vieler Systeme noch nicht vollständig verstanden. Molekulare Simulationen können diese experimentellen Messungen ergänzen. Sie ermöglichen einen Einblick in molekulare Systeme und ihre Funktion auf atomarer Ebene. Die bisherigen Modellierungsmethoden für Protein-Fluorophor-Systeme sind größtenteils Näherungen, die nur für spezielle Anwendungen verwendbar sind oder zu rechenaufwändig um alle relevanten Bewegungen zu modellieren. Diese Arbeit stellt eine neue Methode für die Simulation der Dynamik in Protein-Fluorophor-Systemen vor. Sie basiert auf recheneffizienten vereinfachten Molekulardynamiksimulationen. Mit nur wenigen Parametern bietet die Methode eine realistische Beschreibung des Systems, die quantitativ mit Experimenten übereinstimmt. Sie ermöglicht den direkten Vergleich von Simulationen mit experimentellen Daten und somit eine bessere Planung und Interpretation von Experimenten. Gleichzeitig liefert sie Informationen über die zugrundeliegende Dynamik der Systeme. Diese Arbeit präsentiert ein systematisches Simulationsprotokoll für die Modellierung von Protein-Fluorophor-Systemen in silico, welches für die Erforschung von vielen biologisch relevanten Anwendungen verwendet werden kann. Sie zeigt wie Experimente und Simulationen einander ergänzen, um neue Einblicke in Dynamik und Funktion von Biomolekülen zu erhalten